82 citations · 422 across the 25 of their papers we have counts for
25 papers
An automated activity classification tool for optical galaxy spectra
C. Daoutis, A. Zezas, E. Kyritsis +2
Reliable, versatile galaxy activity diagnostics are essential for understanding galaxy evolution. Traditional methods frequently necessitate extensive preprocessing, such as starli…
A machine-learning photometric classifier for massive stars in nearby galaxies II. The catalog
G. Maravelias, A. Z. Bonanos, K. Antoniadis +8
Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massiv…
From seagull to hummingbird: New diagnostic methods for resolving galaxy activity
C. Daoutis, A. Zezas, E. Kyritsis +2
Context. A major challenge in astrophysics is classifying galaxies by their activity. Current methods often require multiple diagnostics to capture the full range of galactic activ…
Using machine learning to investigate the populations of dusty evolved stars in various metallicities
Grigoris Maravelias, Alceste Z. Bonanos, Frank Tramper +7
Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both f…
A machine-learning photometric classifier for massive stars in nearby galaxies I. The method
Grigoris Maravelias, Alceste Z. Bonanos, Frank Tramper +3
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss.…
A new automated tool for the spectral classification of OB stars
E. Kyritsis, G. Maravelias, A. Zezas +3
(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic…